[Feature] Support iSAID aerial dataset. (#1115)

* support iSAID aerial dataset

* Update and rename docs/dataset_prepare.md to 博士/dataset_prepare.md

* Update dataset_prepare.md

* fix typo

* fix typo

* fix typo

* remove imgviz

* fix wrong order in annotation name

* upload models&logs

* upload models&logs

* add load_annotations

* fix unittest coverage

* fix unittest coverage

* fix correct crop size in config

* fix iSAID unit test

* fix iSAID unit test

* fix typos

* fix wrong crop size in readme

* use smaller figure as test data

* add smaller dataset in test data

* add blank in docs

* use 0 bytes pseudo data

* add footnote and comments for crop size

* change iSAID to isaid and add default value in it

* change iSAID to isaid in _base_

Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>

README_zh-CN.md

@@ -0,0 +1,212 @@
+<div align="center">
+  <img src="resources/mmseg-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab 官网</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab 开放平台</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+</div>
+<br />
+
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/)
+[![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/zh_CN/latest/)
+[![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation)
+[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
+
+文档: https://mmsegmentation.readthedocs.io/zh_CN/latest
+
+[English](README.md) | 简体中文
+
+## 简介
+
+MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 OpenMMLab 项目的一部分。
+
+主分支代码目前支持 PyTorch 1.5 以上的版本。
+
+![示例图片](resources/seg_demo.gif)
+
+### 主要特性
+
+- **统一的基准平台**
+
+  我们将各种各样的语义分割算法集成到了一个统一的工具箱，进行基准测试。
+
+- **模块化设计**
+
+  MMSegmentation 将分割框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的分割模型。
+
+- **丰富的即插即用的算法和模型**
+
+  MMSegmentation 支持了众多主流的和最新的检测算法，例如 PSPNet，DeepLabV3，PSANet，DeepLabV3+ 等.
+
+- **速度快**
+
+  训练速度比其他语义分割代码库更快或者相当。
+
+## 开源许可证
+
+该项目采用 [Apache 2.0 开源许可证](LICENSE)。
+
+## 更新日志
+
+最新版本 v0.21.1 在 2022.2.9 发布。
+如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
+
+## 基准测试和模型库
+
+测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
+
+已支持的骨干网络：
+
+- [x] ResNet (CVPR'2016)
+- [x] ResNeXt (CVPR'2017)
+- [x] [HRNet (CVPR'2019)](configs/hrnet)
+- [x] [ResNeSt (ArXiv'2020)](configs/resnest)
+- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2)
+- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
+- [x] [Vision Transformer (ICLR'2021)](configs/vit)
+- [x] [Swin Transformer (ICCV'2021)](configs/swin)
+- [x] [Twins (NeurIPS'2021)](configs/twins)
+
+已支持的算法：
+
+- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
+- [x] [ERFNet (T-ITS'2017)](configs/erfnet)
+- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
+- [x] [PSPNet (CVPR'2017)](configs/pspnet)
+- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
+- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
+- [x] [PSANet (ECCV'2018)](configs/psanet)
+- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
+- [x] [UPerNet (ECCV'2018)](configs/upernet)
+- [x] [ICNet (ECCV'2018)](configs/icnet)
+- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
+- [x] [EncNet (CVPR'2018)](configs/encnet)
+- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
+- [x] [DANet (CVPR'2019)](configs/danet)
+- [x] [APCNet (CVPR'2019)](configs/apcnet)
+- [x] [EMANet (ICCV'2019)](configs/emanet)
+- [x] [CCNet (ICCV'2019)](configs/ccnet)
+- [x] [DMNet (ICCV'2019)](configs/dmnet)
+- [x] [ANN (ICCV'2019)](configs/ann)
+- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
+- [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
+- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
+- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
+- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
+- [x] [DNLNet (ECCV'2020)](configs/dnlnet)
+- [x] [PointRend (CVPR'2020)](configs/point_rend)
+- [x] [CGNet (TIP'2020)](configs/cgnet)
+- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
+- [x] [STDC (CVPR'2021)](configs/stdc)
+- [x] [SETR (CVPR'2021)](configs/setr)
+- [x] [DPT (ArXiv'2021)](configs/dpt)
+- [x] [Segmenter (ICCV'2021)](configs/segmenter)
+- [x] [SegFormer (NeurIPS'2021)](configs/segformer)
+
+已支持的数据集：
+
+- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#cityscapes)
+- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-voc)
+- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#ade20k)
+- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-context)
+- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-10k)
+- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-164k)
+- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#chase-db1)
+- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#drive)
+- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#hrf)
+- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#stare)
+- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#dark-zurich)
+- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#nighttime-driving)
+- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#loveda)
+- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-potsdam)
+- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
+- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isaid)
+
+## 安装
+
+请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/dataset_prepare.md)处理数据。
+
+## 快速入门
+
+请参考[训练教程](docs/zh_cn/train.md)和[测试教程](docs/zh_cn/inference.md)学习 MMSegmentation 的基本使用。
+我们也提供了一些进阶教程，内容覆盖了[增加自定义数据集](docs/zh_cn/tutorials/customize_datasets.md)，[设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)，[增加自定义模型](docs/zh_cn/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)。
+除此之外，我们也提供了很多实用的[训练技巧说明](docs/zh_cn/tutorials/training_tricks.md)和模型部署相关的[有用的工具](docs/zh_cn/useful_tools.md)。
+
+同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb)。
+
+## 引用
+
+如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMSegmentation。
+
+```bibtex
+@misc{mmseg2020,
+    title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
+    author={MMSegmentation Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
+    year={2020}
+}
+```
+
+## 贡献指南
+
+我们感谢所有的贡献者为改进和提升 MMSegmentation 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
+
+## 致谢
+
+MMSegmentation 是一个由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
+
+## OpenMMLab 的其他项目
+
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 生成模型工具箱
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+
+## 欢迎加入 OpenMMLab 社区
+
+ 扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 [OpenMMLab 团队](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) 以及 [MMSegmentation](https://jq.qq.com/?_wv=1027&k=ukevz6Ie) 的 QQ 群。
+
+ <div align="center">
+ <img src="docs/zh_cn/imgs/zhihu_qrcode.jpg" height="400" />  <img src="docs/zh_cn/imgs/qq_group_qrcode.jpg" height="400" />  <img src="docs/zh_cn/imgs/seggroup_qrcode.jpg" height="400" />
+ </div>
+
+ 我们会在 OpenMMLab 社区为大家
+
+- 📢 分享 AI 框架的前沿核心技术
+- 💻 解读 PyTorch 常用模块源码
+- 📰 发布 OpenMMLab 的相关新闻
+- 🚀 介绍 OpenMMLab 开发的前沿算法
+- 🏃 获取更高效的问题答疑和意见反馈
+- 🔥 提供与各行各业开发者充分交流的平台
+
+ 干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬

configs/_base_/datasets/isaid.py

@@ -0,0 +1,62 @@
+# dataset settings
+dataset_type = 'iSAIDDataset'
+data_root = 'data/iSAID'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+"""
+This crop_size setting is followed by the implementation of
+`PointFlow: Flowing Semantics Through Points for Aerial Image
+Segmentation <https://arxiv.org/pdf/2103.06564.pdf>`_.
+"""
+
+crop_size = (896, 896)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(896, 896), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(896, 896),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img_dir/train',
+        ann_dir='ann_dir/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img_dir/val',
+        ann_dir='ann_dir/val',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img_dir/val',
+        ann_dir='ann_dir/val',
+        pipeline=test_pipeline))

configs/deeplabv3plus/README.md

@@ -114,8 +114,16 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur
 | DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.36      | 26.91             | 73.97 |         75.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json)     |
 | DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.83       | 18.59          | 73.06 |         74.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
 
+### iSAID
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-18-D8  | 896x896   |   80000 | 6.19      | 24.81             | 61.35 |         62.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json)     |
+| DeepLabV3+ | R-50-D8  | 896x896   |   80000 | 21.45      | 8.42             | 67.06 |         68.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json)     |
+
 Note:
 
 - `D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
 - `MG-124` stands for multi-grid dilation in the last stage of ResNet.
 - `FP16` means Mixed Precision (FP16) is adopted in training.
+- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)

configs/deeplabv3plus/deeplabv3plus.yml

@@ -10,6 +10,7 @@ Collections:
     - LoveDA
     - Potsdam
     - Vaihingen
+    - iSAID
   Paper:
     URL: https://arxiv.org/abs/1802.02611
     Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
@@ -803,3 +804,47 @@ Models:
       mIoU(ms+flip): 74.14
   Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth
+- Name: deeplabv3plus_r18-d8_4x4_896x896_80k_isaid
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-18-D8
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 40.31
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 6.19
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 61.35
+      mIoU(ms+flip): 62.61
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth
+- Name: deeplabv3plus_r50-d8_4x4_896x896_80k_isaid
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 118.76
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 21.45
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 67.06
+      mIoU(ms+flip): 68.02
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth

configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py

@@ -0,0 +1,11 @@
+_base_ = './deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py'
+model = dict(
+    pretrained='open-mmlab://resnet18_v1c',
+    backbone=dict(depth=18),
+    decode_head=dict(
+        c1_in_channels=64,
+        c1_channels=12,
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))

configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py

@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/isaid.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=16), auxiliary_head=dict(num_classes=16))

configs/hrnet/README.md

@@ -107,3 +107,15 @@ High-resolution representations are essential for position-sensitive vision prob
 | FCN | HRNetV2p-W18-Small  | 512x512   |   80000 | 1.58      | 38.11             | 71.81 |         73.1 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json)     |
 | FCN | HRNetV2p-W18  | 512x512   |   80000 | 2.76      | 19.55             | 72.57 |         74.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json)     |
 | FCN | HRNetV2p-W48 | 512x512   |   80000 | 6.20       | 17.25          | 72.50 |         73.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json) |
+
+### iSAID
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FCN | HRNetV2p-W18-Small  | 896x896   |   80000 | 4.95      | 13.84            | 62.30 |         62.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json)     |
+| FCN | HRNetV2p-W18  | 896x896   |   80000 | 8.30      | 7.71             | 65.06 |         65.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json)     |
+| FCN | HRNetV2p-W48 | 896x896   |   80000 | 16.89       | 7.34          | 67.80 |         68.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json) |
+
+Note:
+
+- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)

configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py

@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/fcn_hr18.py', '../_base_/datasets/isaid.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(decode_head=dict(num_classes=16))

configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py

@@ -0,0 +1,9 @@
+_base_ = './fcn_hr18_4x4_896x896_80k_isaid.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18_small',
+    backbone=dict(
+        extra=dict(
+            stage1=dict(num_blocks=(2, )),
+            stage2=dict(num_blocks=(2, 2)),
+            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
+            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_4x4_896x896_80k_isaid.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

configs/hrnet/hrnet.yml

@@ -10,6 +10,7 @@ Collections:
     - LoveDA
     - Potsdam
     - Vaihingen
+    - iSAID
   Paper:
     URL: https://arxiv.org/abs/1908.07919
     Title: Deep High-Resolution Representation Learning for Human Pose Estimation
@@ -648,3 +649,69 @@ Models:
       mIoU(ms+flip): 73.52
   Config: configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
+- Name: fcn_hr18s_4x4_896x896_80k_isaid
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 72.25
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 4.95
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 62.3
+      mIoU(ms+flip): 62.97
+  Config: configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
+- Name: fcn_hr18_4x4_896x896_80k_isaid
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 129.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 8.3
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 65.06
+      mIoU(ms+flip): 65.6
+  Config: configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
+- Name: fcn_hr48_4x4_896x896_80k_isaid
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 136.24
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 16.89
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 67.8
+      mIoU(ms+flip): 68.53
+  Config: configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth

configs/pspnet/README.md

@@ -148,6 +148,14 @@ We support evaluation results on these two datasets using models above trained o
 | PSPNet | R-50-D8  | 512x512   |   80000 | 6.14      | 30.29             | 72.36 |         73.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json)     |
 | PSPNet | R-101-D8 | 512x512   |   80000 | 9.61       | 19.97          | 72.61 |         74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806.log.json) |
 
+### iSAID
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| PSPNet | R-18-D8  | 896x896   |   80000 | 4.52      | 26.91             | 60.22 |         61.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json)     |
+| PSPNet | R-50-D8  | 896x896   |   80000 | 16.58      | 8.88             | 65.36 |         66.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629.log.json)     |
+
 Note:
 
 - `FP16` means Mixed Precision (FP16) is adopted in training.
+- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)

configs/pspnet/pspnet.yml

@@ -13,6 +13,7 @@ Collections:
     - LoveDA
     - Potsdam
     - Vaihingen
+    - iSAID
   Paper:
     URL: https://arxiv.org/abs/1612.01105
     Title: Pyramid Scene Parsing Network
@@ -942,3 +943,47 @@ Models:
       mIoU(ms+flip): 74.18
   Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth
+- Name: pspnet_r18-d8_4x4_896x896_80k_isaid
+  In Collection: pspnet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 37.16
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 4.52
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 60.22
+      mIoU(ms+flip): 61.25
+  Config: configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth
+- Name: pspnet_r50-d8_4x4_896x896_80k_isaid
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 112.61
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 16.58
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 65.36
+      mIoU(ms+flip): 66.48
+  Config: configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth

configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py

@@ -0,0 +1,9 @@
+_base_ = './pspnet_r50-d8_4x4_896x896_80k_isaid.py'
+model = dict(
+    pretrained='open-mmlab://resnet18_v1c',
+    backbone=dict(depth=18),
+    decode_head=dict(
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))

configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py

@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/isaid.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=16), auxiliary_head=dict(num_classes=16))

docs/en/dataset_prepare.md

@@ -123,6 +123,21 @@ mmsegmentation
 │   │   ├── ann_dir
 │   │   │   ├── train
 │   │   │   ├── val
+│   ├── vaihingen
+│   │   ├── img_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   ├── ann_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   ├── iSAID
+│   │   ├── img_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   │   ├── test
+│   │   ├── ann_dir
+│   │   │   ├── train
+│   │   │   ├── val
 ```
 
 ### Cityscapes
@@ -325,3 +340,38 @@ python tools/convert_datasets/vaihingen.py /path/to/vaihingen
 ```
 
 In our default setting (`clip_size` =512, `stride_size`=256), it will generate 344 images for training and 398 images for validation.
+
+### iSAID
+The data images could be download from [DOTA-v1.0](https://captain-whu.github.io/DOTA/dataset.html) (train/val/test)
+
+The data annotations could be download from [iSAID](https://captain-whu.github.io/iSAID/dataset.html) (train/val)
+
+The dataset is a Large-scale Dataset for Instance Segmentation (also have segmantic segmentation) in Aerial Images.
+
+You may need to follow the following structure for dataset preparation after downloading iSAID dataset.
+
+```
+│   ├── iSAID
+│   │   ├── train
+│   │   │   ├── images
+│   │   │   │   ├── part1.zip
+│   │   │   │   ├── part2.zip
+│   │   │   │   ├── part3.zip
+│   │   │   ├── Semantic_masks
+│   │   │   │   ├── images.zip
+│   │   ├── val
+│   │   │   ├── images
+│   │   │   │   ├── part1.zip
+│   │   │   ├── Semantic_masks
+│   │   │   │   ├── images.zip
+│   │   ├── test
+│   │   │   ├── images
+│   │   │   │   ├── part1.zip
+│   │   │   │   ├── part2.zip
+```
+
+```shell
+python tools/convert_datasets/isaid.py /path/to/iSAID
+```
+
+In our default setting (`clip_size` =512, `stride_size`=256), it will generate 33978 images for training and 11644 images for validation.

docs/zh_cn/dataset_prepare.md

@@ -104,6 +104,21 @@ mmsegmentation
 │   │   ├── ann_dir
 │   │   │   ├── train
 │   │   │   ├── val
+│   ├── vaihingen
+│   │   ├── img_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   ├── ann_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   ├── iSAID
+│   │   ├── img_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   │   ├── test
+│   │   ├── ann_dir
+│   │   │   ├── train
+│   │   │   ├── val
 ```
 
 ### Cityscapes
@@ -265,4 +280,39 @@ python tools/convert_datasets/potsdam.py /path/to/potsdam
 python tools/convert_datasets/vaihingen.py /path/to/vaihingen
 ```
 
-使用我们默认的配置 (`clip_size` =512, `stride_size`=256)， 将生成 344 张图片的训练集和 398 张图片的验证集。
+使用我们默认的配置 (`clip_size`=512, `stride_size`=256)， 将生成 344 张图片的训练集和 398 张图片的验证集。
+
+### iSAID
+iSAID 数据集(训练集/验证集/测试集)的图像可以从 [DOTA-v1.0](https://captain-whu.github.io/DOTA/dataset.html) 下载.
+
+iSAID 数据集(训练集/验证集)的注释可以从 [iSAID](https://captain-whu.github.io/iSAID/dataset.html) 下载.
+
+该数据集是一个大规模的实例分割(也可以用于语义分割)的遥感数据集.
+
+下载后，在数据集转换前，您需要将数据集文件夹调整成如下格式.
+
+```
+│   ├── iSAID
+│   │   ├── train
+│   │   │   ├── images
+│   │   │   │   ├── part1.zip
+│   │   │   │   ├── part2.zip
+│   │   │   │   ├── part3.zip
+│   │   │   ├── Semantic_masks
+│   │   │   │   ├── images.zip
+│   │   ├── val
+│   │   │   ├── images
+│   │   │   │   ├── part1.zip
+│   │   │   ├── Semantic_masks
+│   │   │   │   ├── images.zip
+│   │   ├── test
+│   │   │   ├── images
+│   │   │   │   ├── part1.zip
+│   │   │   │   ├── part2.zip
+```
+
+```shell
+python tools/convert_datasets/isaid.py /path/to/iSAID
+```
+
+使用我们默认的配置 (`patch_width`=896, `patch_height`=896,　`overlap_area`=384)， 将生成 33978 张图片的训练集和 11644 张图片的验证集。

mmseg/core/evaluation/class_names.py

@@ -111,6 +111,16 @@ def vaihingen_classes():
     ]
 
 
+def isaid_classes():
+    """iSAID class names for external use."""
+    return [
+        'background', 'ship', 'store_tank', 'baseball_diamond', 'tennis_court',
+        'basketball_court', 'Ground_Track_Field', 'Bridge', 'Large_Vehicle',
+        'Small_Vehicle', 'Helicopter', 'Swimming_pool', 'Roundabout',
+        'Soccer_ball_field', 'plane', 'Harbor'
+    ]
+
+
 def cityscapes_palette():
     """Cityscapes palette for external use."""
     return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
@@ -236,6 +246,15 @@ def vaihingen_palette():
             [255, 255, 0], [255, 0, 0]]
 
 
+def isaid_palette():
+    """iSAID palette for external use."""
+    return [[0, 0, 0], [0, 0, 63], [0, 63, 63], [0, 63, 0], [0, 63, 127],
+            [0, 63, 191], [0, 63, 255], [0, 127, 63], [0, 127,
+                                                       127], [0, 0, 127],
+            [0, 0, 191], [0, 0, 255], [0, 191, 127], [0, 127, 191],
+            [0, 127, 255], [0, 100, 155]]
+
+
 dataset_aliases = {
     'cityscapes': ['cityscapes'],
     'ade': ['ade', 'ade20k'],
@@ -247,7 +266,8 @@ dataset_aliases = {
         'cocostuff', 'cocostuff10k', 'cocostuff164k', 'coco-stuff',
         'coco-stuff10k', 'coco-stuff164k', 'coco_stuff', 'coco_stuff10k',
         'coco_stuff164k'
-    ]
+    ],
+    'isaid': ['isaid', 'iSAID']
 }
 
 

mmseg/datasets/__init__.py

@@ -10,6 +10,7 @@ from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset,
                                RepeatDataset)
 from .drive import DRIVEDataset
 from .hrf import HRFDataset
+from .isaid import iSAIDDataset
 from .isprs import ISPRSDataset
 from .loveda import LoveDADataset
 from .night_driving import NightDrivingDataset
@@ -25,5 +26,5 @@ __all__ = [
     'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
     'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
     'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset',
-    'ISPRSDataset', 'PotsdamDataset'
+    'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset'
 ]

mmseg/datasets/isaid.py

@@ -0,0 +1,82 @@
+import os.path as osp
+
+import mmcv
+from mmcv.utils import print_log
+
+from ..utils import get_root_logger
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class iSAIDDataset(CustomDataset):
+    """ iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
+    In segmentation map annotation for iSAID dataset, which is included
+    in 16 categories. ``reduce_zero_label`` is fixed to False. The
+    ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
+    '_manual1.png'.
+    """
+
+    CLASSES = ('background', 'ship', 'store_tank', 'baseball_diamond',
+               'tennis_court', 'basketball_court', 'Ground_Track_Field',
+               'Bridge', 'Large_Vehicle', 'Small_Vehicle', 'Helicopter',
+               'Swimming_pool', 'Roundabout', 'Soccer_ball_field', 'plane',
+               'Harbor')
+
+    PALETTE = [[0, 0, 0], [0, 0, 63], [0, 63, 63], [0, 63, 0], [0, 63, 127],
+               [0, 63, 191], [0, 63, 255], [0, 127, 63], [0, 127, 127],
+               [0, 0, 127], [0, 0, 191], [0, 0, 255], [0, 191, 127],
+               [0, 127, 191], [0, 127, 255], [0, 100, 155]]
+
+    def __init__(self, **kwargs):
+        super(iSAIDDataset, self).__init__(
+            img_suffix='.png',
+            seg_map_suffix='.png',
+            ignore_index=255,
+            **kwargs)
+        assert osp.exists(self.img_dir)
+
+    def load_annotations(self,
+                         img_dir,
+                         img_suffix,
+                         ann_dir,
+                         seg_map_suffix=None,
+                         split=None):
+        """Load annotation from directory.
+
+        Args:
+            img_dir (str): Path to image directory
+            img_suffix (str): Suffix of images.
+            ann_dir (str|None): Path to annotation directory.
+            seg_map_suffix (str|None): Suffix of segmentation maps.
+            split (str|None): Split txt file. If split is specified, only file
+                with suffix in the splits will be loaded. Otherwise, all images
+                in img_dir/ann_dir will be loaded. Default: None
+
+        Returns:
+            list[dict]: All image info of dataset.
+        """
+
+        img_infos = []
+        if split is not None:
+            with open(split) as f:
+                for line in f:
+                    name = line.strip()
+                    img_info = dict(filename=name + img_suffix)
+                    if ann_dir is not None:
+                        ann_name = name + '_instance_color_RGB'
+                        seg_map = ann_name + seg_map_suffix
+                        img_info['ann'] = dict(seg_map=seg_map)
+                    img_infos.append(img_info)
+        else:
+            for img in mmcv.scandir(img_dir, img_suffix, recursive=True):
+                img_info = dict(filename=img)
+                if ann_dir is not None:
+                    seg_img = img
+                    seg_map = seg_img.replace(
+                        img_suffix, '_instance_color_RGB' + seg_map_suffix)
+                    img_info['ann'] = dict(seg_map=seg_map)
+                img_infos.append(img_info)
+
+        print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger())
+        return img_infos

setup.cfg

@@ -16,4 +16,4 @@ default_section = THIRDPARTY
 skip = *.po,*.ts,*.ipynb
 count =
 quiet-level = 3
-ignore-words-list = formating,sur,hist
+ignore-words-list = formating,sur,hist,dota

tests/data/pseudo_isaid_dataset/splits/train.txt

@@ -0,0 +1 @@
+P0000_0_896_1536_2432

tests/data/pseudo_isaid_dataset/splits/val.txt

@@ -0,0 +1 @@
+P0000_0_896_1024_1920

tests/test_data/test_dataset.py

@@ -16,7 +16,7 @@ from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
                             COCOStuffDataset, ConcatDataset, CustomDataset,
                             ISPRSDataset, LoveDADataset, MultiImageMixDataset,
                             PascalVOCDataset, PotsdamDataset, RepeatDataset,
-                            build_dataset)
+                            build_dataset, iSAIDDataset)
 
 
 def test_classes():
@@ -25,10 +25,11 @@ def test_classes():
         'pascal_voc')
     assert list(
         ADE20KDataset.CLASSES) == get_classes('ade') == get_classes('ade20k')
+    assert list(COCOStuffDataset.CLASSES) == get_classes('cocostuff')
     assert list(LoveDADataset.CLASSES) == get_classes('loveda')
     assert list(PotsdamDataset.CLASSES) == get_classes('potsdam')
     assert list(ISPRSDataset.CLASSES) == get_classes('vaihingen')
-    assert list(COCOStuffDataset.CLASSES) == get_classes('cocostuff')
+    assert list(iSAIDDataset.CLASSES) == get_classes('isaid')
 
     with pytest.raises(ValueError):
         get_classes('unsupported')
@@ -73,6 +74,7 @@ def test_palette():
     assert LoveDADataset.PALETTE == get_palette('loveda')
     assert PotsdamDataset.PALETTE == get_palette('potsdam')
     assert COCOStuffDataset.PALETTE == get_palette('cocostuff')
+    assert iSAIDDataset.PALETTE == get_palette('isaid')
 
     with pytest.raises(ValueError):
         get_palette('unsupported')
@@ -730,6 +732,27 @@ def test_vaihingen():
     assert len(test_dataset) == 1
 
 
+def test_isaid():
+    test_dataset = iSAIDDataset(
+        pipeline=[],
+        img_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
+        ann_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'))
+    assert len(test_dataset) == 2
+    isaid_info = test_dataset.load_annotations(
+        img_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
+        img_suffix='.png',
+        ann_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'),
+        seg_map_suffix='.png',
+        split=osp.join(
+            osp.dirname(__file__),
+            '../data/pseudo_isaid_dataset/splits/train.txt'))
+    assert len(isaid_info) == 1
+
+
 @patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
 @patch('mmseg.datasets.CustomDataset.__getitem__',
        MagicMock(side_effect=lambda idx: idx))

tools/convert_datasets/isaid.py

@@ -0,0 +1,244 @@
+import argparse
+import glob
+import os
+import os.path as osp
+import shutil
+import tempfile
+import zipfile
+
+import mmcv
+import numpy as np
+from PIL import Image
+
+iSAID_palette = \
+    {
+        0: (0, 0, 0),
+        1: (0, 0, 63),
+        2: (0, 63, 63),
+        3: (0, 63, 0),
+        4: (0, 63, 127),
+        5: (0, 63, 191),
+        6: (0, 63, 255),
+        7: (0, 127, 63),
+        8: (0, 127, 127),
+        9: (0, 0, 127),
+        10: (0, 0, 191),
+        11: (0, 0, 255),
+        12: (0, 191, 127),
+        13: (0, 127, 191),
+        14: (0, 127, 255),
+        15: (0, 100, 155)
+    }
+
+iSAID_invert_palette = {v: k for k, v in iSAID_palette.items()}
+
+
+def iSAID_convert_from_color(arr_3d, palette=iSAID_invert_palette):
+    """RGB-color encoding to grayscale labels."""
+    arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
+
+    for c, i in palette.items():
+        m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
+        arr_2d[m] = i
+
+    return arr_2d
+
+
+def slide_crop_image(src_path, out_dir, mode, patch_H, patch_W, overlap):
+    img = np.asarray(Image.open(src_path).convert('RGB'))
+
+    img_H, img_W, _ = img.shape
+
+    if img_H < patch_H and img_W > patch_W:
+
+        img = mmcv.impad(img, shape=(patch_H, img_W), pad_val=0)
+
+        img_H, img_W, _ = img.shape
+
+    elif img_H > patch_H and img_W < patch_W:
+
+        img = mmcv.impad(img, shape=(img_H, patch_W), pad_val=0)
+
+        img_H, img_W, _ = img.shape
+
+    elif img_H < patch_H and img_W < patch_W:
+
+        img = mmcv.impad(img, shape=(patch_H, patch_W), pad_val=0)
+
+        img_H, img_W, _ = img.shape
+
+    for x in range(0, img_W, patch_W - overlap):
+        for y in range(0, img_H, patch_H - overlap):
+            x_str = x
+            x_end = x + patch_W
+            if x_end > img_W:
+                diff_x = x_end - img_W
+                x_str -= diff_x
+                x_end = img_W
+            y_str = y
+            y_end = y + patch_H
+            if y_end > img_H:
+                diff_y = y_end - img_H
+                y_str -= diff_y
+                y_end = img_H
+
+            img_patch = img[y_str:y_end, x_str:x_end, :]
+            img_patch = Image.fromarray(img_patch.astype(np.uint8))
+            image = osp.splitext(
+                src_path.split('/')[-1])[0] + '_' + str(y_str) + '_' + str(
+                    y_end) + '_' + str(x_str) + '_' + str(x_end) + '.png'
+            # print(image)
+            save_path_image = osp.join(out_dir, 'img_dir', mode, str(image))
+            img_patch.save(save_path_image)
+
+
+def slide_crop_label(src_path, out_dir, mode, patch_H, patch_W, overlap):
+    label = mmcv.imread(src_path, channel_order='rgb')
+    label = iSAID_convert_from_color(label)
+    img_H, img_W = label.shape
+
+    if img_H < patch_H and img_W > patch_W:
+
+        label = mmcv.impad(label, shape=(patch_H, img_W), pad_val=255)
+
+        img_H = patch_H
+
+    elif img_H > patch_H and img_W < patch_W:
+
+        label = mmcv.impad(label, shape=(img_H, patch_W), pad_val=255)
+
+        img_W = patch_W
+
+    elif img_H < patch_H and img_W < patch_W:
+
+        label = mmcv.impad(label, shape=(patch_H, patch_W), pad_val=255)
+
+        img_H = patch_H
+        img_W = patch_W
+
+    for x in range(0, img_W, patch_W - overlap):
+        for y in range(0, img_H, patch_H - overlap):
+            x_str = x
+            x_end = x + patch_W
+            if x_end > img_W:
+                diff_x = x_end - img_W
+                x_str -= diff_x
+                x_end = img_W
+            y_str = y
+            y_end = y + patch_H
+            if y_end > img_H:
+                diff_y = y_end - img_H
+                y_str -= diff_y
+                y_end = img_H
+
+            lab_patch = label[y_str:y_end, x_str:x_end]
+            lab_patch = Image.fromarray(lab_patch.astype(np.uint8), mode='P')
+
+            image = osp.splitext(src_path.split('/')[-1])[0].split(
+                '_')[0] + '_' + str(y_str) + '_' + str(y_end) + '_' + str(
+                    x_str) + '_' + str(x_end) + '_instance_color_RGB' + '.png'
+            lab_patch.save(osp.join(out_dir, 'ann_dir', mode, str(image)))
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert iSAID dataset to mmsegmentation format')
+    parser.add_argument('dataset_path', help='iSAID folder path')
+    parser.add_argument('--tmp_dir', help='path of the temporary directory')
+    parser.add_argument('-o', '--out_dir', help='output path')
+
+    parser.add_argument(
+        '--patch_width',
+        default=896,
+        type=int,
+        help='Width of the cropped image patch')
+    parser.add_argument(
+        '--patch_height',
+        default=896,
+        type=int,
+        help='Height of the cropped image patch')
+    parser.add_argument(
+        '--overlap_area', default=384, type=int, help='Overlap area')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    dataset_path = args.dataset_path
+    # image patch width and height
+    patch_H, patch_W = args.patch_width, args.patch_height
+
+    overlap = args.overlap_area  # overlap area
+
+    if args.out_dir is None:
+        out_dir = osp.join('data', 'iSAID')
+    else:
+        out_dir = args.out_dir
+
+    print('Making directories...')
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
+
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'test'))
+
+    assert os.path.exists(os.path.join(dataset_path, 'train')), \
+        'train is not in {}'.format(dataset_path)
+    assert os.path.exists(os.path.join(dataset_path, 'val')), \
+        'val is not in {}'.format(dataset_path)
+    assert os.path.exists(os.path.join(dataset_path, 'test')), \
+        'test is not in {}'.format(dataset_path)
+
+    with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
+        for dataset_mode in ['train', 'val', 'test']:
+
+            # for dataset_mode in [ 'test']:
+            print('Extracting  {}ing.zip...'.format(dataset_mode))
+            img_zipp_list = glob.glob(
+                os.path.join(dataset_path, dataset_mode, 'images', '*.zip'))
+            print('Find the data', img_zipp_list)
+            for img_zipp in img_zipp_list:
+                zip_file = zipfile.ZipFile(img_zipp)
+                zip_file.extractall(os.path.join(tmp_dir, dataset_mode, 'img'))
+            src_path_list = glob.glob(
+                os.path.join(tmp_dir, dataset_mode, 'img', 'images', '*.png'))
+
+            src_prog_bar = mmcv.ProgressBar(len(src_path_list))
+            for i, img_path in enumerate(src_path_list):
+                if dataset_mode != 'test':
+                    slide_crop_image(img_path, out_dir, dataset_mode, patch_H,
+                                     patch_W, overlap)
+
+                else:
+                    shutil.move(img_path,
+                                os.path.join(out_dir, 'img_dir', dataset_mode))
+                src_prog_bar.update()
+
+            if dataset_mode != 'test':
+                label_zipp_list = glob.glob(
+                    os.path.join(dataset_path, dataset_mode, 'Semantic_masks',
+                                 '*.zip'))
+                for label_zipp in label_zipp_list:
+                    zip_file = zipfile.ZipFile(label_zipp)
+                    zip_file.extractall(
+                        os.path.join(tmp_dir, dataset_mode, 'lab'))
+
+                lab_path_list = glob.glob(
+                    os.path.join(tmp_dir, dataset_mode, 'lab', 'images',
+                                 '*.png'))
+                lab_prog_bar = mmcv.ProgressBar(len(lab_path_list))
+                for i, lab_path in enumerate(lab_path_list):
+                    slide_crop_label(lab_path, out_dir, dataset_mode, patch_H,
+                                     patch_W, overlap)
+                    lab_prog_bar.update()
+
+        print('Removing the temporary files...')
+
+    print('Done!')
+
+
+if __name__ == '__main__':
+    main()